From coffee machine settings to chemical reactions to website AB testing - iterative make-test-learn cycles are ubiquitous. The Bayesian Back End (BayBE) is an open-source experimental planner enabling users to smartly navigate such black-box optimization problems in iterative settings. This tutorial will i) introduce the core concepts enabled by combining Bayesian optimization and machine learning; ii) explain our software design choices, robust tests and open-source libraries this is built on; and iii) provide a short practical hands-on session.